我在解决这个问题时遇到了麻烦，
基本上，我有一个类似import pandas as pd
import numpy as np
from collections import Counter
d = ({
'Time' : ['8:03:00','8:17:00','8:20:00','8:33:00','8:47:00','8:48:00','9:03:00','9:15:00','9:18:00','9:33:00','9:45:00','9:48:00','10:03:00','10:15:00','10:15:00','10:15:00','10:18:00','10:32:00','10:33:00','10:39:00','10:43:00','10:48:00','10:50:00','11:03:00','11:03:00','11:07:00','11:25:00','11:27:00','11:42:00','11:48:00','11:51:00','11:57:00','12:00:00','12:08:00','12:15:00','12:17:00','12:25:00','12:30:00','12:35:00','12:39:00','12:47:00','12:52:00','12:55:00','13:00:00','13:03:00','13:07:00','13:12:00','13:15:00','13:22:00','13:27:00','13:27:00'],
'Area' : ['A','A','A','A','A','A','A','A','A','A','A','A','A','A','A','B','A','B','A','A','A','A','B','A','A','B','B','A','B','C','A','B','C','C','A','B','C','C','B','A','C','B','C','C','A','C','B','C','C','A','C'],
'Place' : ['House 1','House 2','House 3','House 1','House 3','House 2','House 1','House 3','House 2','House 1','House 3','House 2','House 1','House 3','House 4','House 1','House 2','House 1','House 1','House 4','House 3','House 2','House 1','House 1','House 4','House 1','House 1','House 4','House 1','House 1','House 4','House 1','House 2','House 1','House 4','House 1','House 1','House 2','House 1','House 4','House 1','House 1','House 3','House 2','House 4','House 1','House 2','House 4','House 1','House 4','House 2'],
'On' : ['1','2','3','3','3','3','3','3','3','3','3','3','3','3','4','5','5','5','5','5','5','4','3','3','3','2','2','2','2','3','3','3','4','4','4','4','4','4','4','4','4','4','4','4','4','4','5','6','6','6','6'],
'Person' : ['Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 1','Person 2','Person 3','Person 1','Person 3','Person 1','Person 2','Person 1','Person 1','Person 3','Person 1','Person 2','Person 3','Person 3','Person 2','Person 3','Person 4','Person 2','Person 3','Person 4','Person 4','Person 2','Person 3','Person 4','Person 4','Person 3','Person 2','Person 4','Person 3','Person 4','Person 4','Person 2','Person 4','Person 3','Person 5','Person 4','Person 2','Person 4'],
})
df = pd.DataFrame(data=d)
def getAssignedPeople(df, areasPerPerson):
areas = df['Area'].values
places = df['Place'].values
times = pd.to_datetime(df['Time']).values
maxPerson = np.ceil(areas.size / float(areasPerPerson)) - 1
assignmentCount = Counter()
assignedPeople = []
assignedPlaces = {}
heldPeople = {}
heldAreas = {}
holdAvailable = True
person = 0
# search for repeated areas. Mark them if the next repeat occurs within an hour
ixrep = np.argmax(np.triu(areas.reshape(-1, 1)==areas, k=1), axis=1)
holds = np.zeros(areas.size, dtype=bool)
holds[ixrep.nonzero()] = (times[ixrep[ixrep.nonzero()]] - times[ixrep.nonzero()]) < np.timedelta64(1, 'h')
for area,place,hold in zip(areas, places, holds):
if (area, place) in assignedPlaces:
# this unique (area, place) has already been assigned to someone
assignedPeople.append(assignedPlaces[(area, place)])
continue
if assignmentCount[person] >= areasPerPerson:
# the current person is already assigned to enough areas, move on to the next
a = heldPeople.pop(person, None)
heldAreas.pop(a, None)
person += 1
if area in heldAreas:
# assign to the person held in this area
p = heldAreas.pop(area)
heldPeople.pop(p)
else:
# get the first non-held person. If we need to hold in this area,
# also make sure the person has at least 2 free assignment slots,
# though if it's the last person assign to them anyway
p = person
while p in heldPeople or (hold and holdAvailable and (areasPerPerson - assignmentCount[p] < 2)) and not p==maxPerson:
p += 1
assignmentCount.update([p])
assignedPlaces[(area, place)] = p
assignedPeople.append(p)
if hold:
if p==maxPerson:
# mark that there are no more people available to perform holds
holdAvailable = False
# this area recurrs in an hour, mark that the person should be held here
heldPeople[p] = area
heldAreas[area] = p
return assignedPeople
def allocatePeople(df, areasPerPerson=3):
assignedPeople = getAssignedPeople(df, areasPerPerson=areasPerPerson)
df = df.copy()
df.loc[:,'Person'] = df['Person'].unique()[assignedPeople]
return df
print(allocatePeople(df))
的url字符串，现在我要做的就是查找并替换 Time Area Place On Person
0 8:03:00 A House 1 1 Person 1
1 8:17:00 A House 2 2 Person 1
2 8:20:00 A House 3 3 Person 1
3 8:33:00 A House 1 3 Person 1
4 8:47:00 A House 3 3 Person 1
5 8:48:00 A House 2 3 Person 1
6 9:03:00 A House 1 3 Person 1
7 9:15:00 A House 3 3 Person 1
8 9:18:00 A House 2 3 Person 1
9 9:33:00 A House 1 3 Person 1
10 9:45:00 A House 3 3 Person 1
11 9:48:00 A House 2 3 Person 1
12 10:03:00 A House 1 3 Person 1
13 10:15:00 A House 3 3 Person 1
14 10:15:00 A House 4 4 Person 2
15 10:15:00 B House 1 5 Person 2
16 10:18:00 A House 2 5 Person 1
17 10:32:00 B House 1 5 Person 2
18 10:33:00 A House 1 5 Person 1
19 10:39:00 A House 4 5 Person 2
20 10:43:00 A House 3 5 Person 1
21 10:48:00 A House 2 4 Person 1
22 10:50:00 B House 1 3 Person 2
23 11:03:00 A House 1 3 Person 1
24 11:03:00 A House 4 3 Person 2
25 11:07:00 B House 1 2 Person 2
26 11:25:00 B House 1 2 Person 2
27 11:27:00 A House 4 2 Person 2
28 11:42:00 B House 1 2 Person 2
29 11:48:00 C House 1 3 Person 2
30 11:51:00 A House 4 3 Person 2
31 11:57:00 B House 1 3 Person 2
32 12:00:00 C House 2 4 Person 3
33 12:08:00 C House 1 4 Person 2
34 12:15:00 A House 4 4 Person 2
35 12:17:00 B House 1 4 Person 2
36 12:25:00 C House 1 4 Person 2
37 12:30:00 C House 2 4 Person 3
38 12:35:00 B House 1 4 Person 2
39 12:39:00 A House 4 4 Person 2
40 12:47:00 C House 1 4 Person 2
41 12:52:00 B House 1 4 Person 2
42 12:55:00 C House 3 4 Person 3
43 13:00:00 C House 2 4 Person 3
44 13:03:00 A House 4 4 Person 2
45 13:07:00 C House 1 4 Person 2
46 13:12:00 B House 2 5 Person 3
47 13:15:00 C House 4 6 Person 4
48 13:22:00 C House 1 6 Person 2
49 13:27:00 A House 4 6 Person 2
50 13:27:00 C House 2 6 Person 3
的所有实例，并用空格替换，以便该字符串变为this%20is%20a%20string。
现在我已经尝试做这样的事情。
%20它将字符串拆分成一个数组，但是我不确定如何将单独的字符串数组转换成一个完整的字符串，每个单词之间都有空格。
任何帮助将不胜感激。
答案 0 :(得分：3)
只需使用join：
str.split('%20').join(" ")
答案 1 :(得分：3)
使用正则表达式
str.replace(/%20/g, ' ');
答案 2 :(得分：3)